In the field of medical imaging, various systems have been developed for generating medical images of various anatomical structures of individuals for the purpose of screening and evaluating medical conditions. These imaging systems include, for example, CT (computed tomography) imaging, MRI (magnetic resonance imaging), X-ray systems, ultrasound systems, PET (positron emission tomography) systems, etc. Each imaging modality may provide unique advantages over other modalities for screening and evaluating certain types of diseases, medical conditions or anatomical abnormalities, including, for example, colonic polyps, aneurisms, lung nodules, calcification on heart or artery tissue, cancer microcalcifications or masses in breast tissue, and various other lesions or abnormalities.
For example, as is well-known in the art, CT (computed tomography) imaging systems can be used to obtain a set of cross-sectional images or 2D “slices” of a ROI (region-of-interest) of a patient for purposes of imaging organs and other anatomies. The CT imaging modality is commonly employed for purposes of diagnosing disease because such modality provides precise images that illustrate the size, shape, and location of various anatomical structures such as organs, soft tissues, and bones, and also enables a more accurate evaluation of lesions and abnormal anatomical structures such as cancer, polyps, etc.
One conventional method that physicians, clinicians, radiologists, etc., use for diagnosing and evaluating medical conditions is to manually review hard-copies (X-ray films, prints, photographs, etc) of medical images that are reconstructed from an acquired image dataset, to discern characteristic features of interest. For example, CT image data that is acquired during a CT examination can be used to produce a set of 2D medical images (X-ray films) that can be viewed to identify potential abnormal anatomical structures or lesions, for example, based upon the skill and knowledge of the reviewing physician, clinician, radiologist, etc. For example, a mammogram procedure may produce medical images that include normal anatomical structures corresponding to breast tissue, but a trained radiologist may be able identify small lesions among these structures that are potentially cancerous. However, a trained radiologist, physician or clinician may misdiagnose a medical condition such as breast cancer due to human error.
Accordingly, various image data processing systems and tools have been developed to assist physicians, clinicians, radiologists, etc, in evaluating medical images to diagnose medical conditions. For example, CAD (computer-aided detection) tools have been developed for various clinical applications to provide automated detection of medical conditions in medical images. In general, CAD systems employ methods for digital signal processing of image data (e.g., CT data) to automatically detect lesions and other abnormal anatomical structures such as colonic polyps, aneurisms, lung nodules, calcification on heart or artery tissue, micro calcifications or masses in breast tissue, etc.
More specifically, conventional CAD tools include methods for analyzing image data to automatically detect and mark regions of features of interest in the image data which are identified as being potential lesions, abnormalities, disease states, etc. When the marked image data is rendered and displayed, the marked regions or features are “marked” or otherwise highlighted to direct the attention of the radiologist to potential medical conditions in medical image.
Although CAD systems can be very useful for diagnostic purposes, various governmental agencies (such as the FDA) and other groups are concerned that physicians may become too dependent on CAD systems and blindly rely on the CAD findings without conducting an independent review/analysis of the medical images to confirm/verify/reject potential medical conditions as indicated by the computer-generated marks. Indeed, if a physician becomes too reliant and trusting of a CAD tool that he/she uses on a regular basis, the physician may misdiagnose a potential medical condition, or otherwise fail to identify a potential medical condition, if the CAD process generates incorrect results. For instance, the results of a CAD analysis can include “false positives” by incorrectly marking normal regions, or the CAD analysis may result in “unmarked” but nonetheless abnormal regions.
In such instances, the physician's blind reliance on incorrect CAD marks could result in significant/substantial changes in a patient management process due to extra testing or biopsies, time lost by the radiologist, increased healthcare costs, trauma to the patient, and lead to a lack of trust in computer-assisted diagnosis systems.